Minyue Li

A Low-Delay Audio Coder with Constrained-Entropy Quantization

We present a low-delay, constrained-entropy, backward adaptive, linear-predictive audio coder with low computational complexity. In contrast to most practical linear-predictive coders, the coder facilitates the exploitation of reverse waterfilling. The coder uses time-invariant quantization step size and constrained-entropy coding, thus eliminating the convergence problems of backward adaptation near signal transitions. Yet rate variations are kept small by the usage of a mixture model density for the signal. The mixture model has the backward adapted model and a second model as components and the component probability is transmitted. Experimental results confirm the advantages of the coder structure and show that the coder provides good overall performance.

Flexcode - flexible audio coding

Modern networks are highly variable and, as a result, source coders are commonly used under conditions that they were not designed for. We address this problem with a source-coding philosophy that aims at the instantaneous re-optimization of a source coder to match a wide range of constraints on rate or quality and a wide range of packet-loss rates. We present a number of technologies that can be reconfigured by solving analytic relations that use the current conditions and a statistical description of the source as input. The technologies include distribution-preserving quantizers, flexible multiple-description quantizers, and a rate distribution scheme. Based on the generic technologies, we created a complete audio coder. Formal listening tests show that the resulting audio coding scheme with full flexibility provides a quality that is on-par with the best standardized codecs for any particular rate.

Multiple Description Distribution Preserving Quantization

The notion of a multiple description quantizer (MDQ) includes providing multiple distortion levels. If a human observer is involved, the design of MDQ requires a suitable distortion measure to achieve a graceful quality degradation in the case of description losses. While the mean squared error is a ubiquitous distortion measure for many classic MDQ schemes, it is known to be perceptually relevant only at low distortions. We propose a new MDQ designed according to an unconventional distortion criterion that combines the mean squared error with a constraint on the probability distribution of the reconstructed signal. The performance of the new MDQ is shown to approach that of the classic MDQ asymptotically as rate increases. However, once applied in the context of transform audio coding, the new MDQ significantly outperforms a classic MDQ in perceptual tests. The new scheme is suitable for a wide range of distortions and renders a seamless transition between coding that preserves signal features and coding of a waveform.

Predictive audio coding using rate-distortion-optimal pre- and post-filtering

A natural approach to audio coding is to use a rate-distortion optimal design combined with a perceptual model. While this approach is common in transform coding, existing predictive-coding based audio coders are generally not optimal and they benefit from heuristically motivated post-filtering. As delay requirements often force the use of predictive coding, we consider audio coding with a pre- and post-filtered predictive structure that was recently proven to be asymptotically optimal in the rate-distortion sense [1]. We show that this audio coding is efficient in achieving the state-of-the-art performance. We also show that the pre-filter plays a relatively minor role. This leads to an analytic approach for optimizing the post-filter and the predictor at each rate, eliminating the need for manual re-tuning whenever a different rate is called for. In a subjective test, the theoretically optimized post-filter provided a better performance than a conventional post-filter.

Audio codingwith power spectral density preserving quantization

The coding of audio-visual signals is generally based on different paradigms for high and low rates. At high rates the signal is approximated directly and at low rates only signal features are transmitted. The recently introduced distribution preserving quantization (DPQ) paradigm provides a seamless transition between these two regimes. In this paper we present a simplified scheme that preserves the power spectral density (PSD) rather than the probability distribution. In a practical system the PSD must be estimated. We show that both forward adaptive and backward adaptive PSD estimation are possible. Our experimental results confirm that preservation of PSD at finite precision leads to a unified coding paradigm that provides effective coding at both high and low rates. An audio coding application shows the perceptual benefits of PSD preserving quantization.